It is to be appreciated that any discussion of documents, devices, acts or knowledge in this specification is included to explain the context of the present invention. Further, the discussion throughout this specification comes about due to the realisation of the inventor and/or the identification of certain related art problems by the inventor. Moreover, any discussion of material such as documents, devices, acts or knowledge in this specification is included to explain the context of the invention in terms of the inventor's knowledge and experience and, accordingly, any such discussion should not be taken as an admission that any of the material forms part of the prior art base or the common general knowledge in the relevant art in Australia, or elsewhere, on or before the priority date of the disclosure and claims herein.
It will also be appreciated that references herein to ‘motion’ are interchangeable with ‘flow’ or ‘velocity’ (being a function of motion over time).
The ability to measure three-dimensional (3D) blood flow fields in vivo is an important capability for studying the effects of blood flow properties on the development, diagnosis and treatment of cardiovascular diseases, such as atherosclerosis. To gain useful information from in vivo blood flow field measurements, non-invasive measurement through optically opaque tissue at high resolution is required.
The development of technologies underpinning in vivo measurements of form and function of the human body are discussed in various reviews. (See for example Fouras A, Kitchen M J, Dubsky S, Lewis R A, Hooper S B and Hourigan K 2009 Journal of Applied Physics Vol. 105).
Currently available techniques for flow field measurement in opaque vessels, such as magnetic resonance imaging based techniques, suffer from poor spatial and temporal resolution, limiting the application of these techniques for in vivo flow analysis. Better results have been achieved with techniques such as Particle image velocimetry (PIV) in which the displacement of tracer particles is determined using statistical cross-correlation of regions within particle image pairs. Several variants exist for volumetric flow analysis, including Tomographic PIV, volumetric particle tracking and Holographic PIV.
PIV Imaging Generally
PIV is well known for accurate measurement of instantaneous velocity fields. PIV techniques using visible light are limited to optically transparent sample. However the use of X-rays with PIV has extended the application of this method to opaque tissue, making this imaging Mode ideal for in vivo blood flow field measurement.
In PIV, regions of fluid containing multiple tracer particles (typically illuminated by a visible wavelength laser) are imaged at two points in time, separated by a known time interval, and processed using correlation software, Specifically the image pairs are allocated into discrete interrogation regions. Cross correlation is performed between image pairs on each interrogation region and statistically, the maximum value of the cross correlation is the most likely particle displacement within the interrogation region.
In recent years PIV has been combined with X-ray imaging. The penetrating power of X-rays allows flow to be measured within opaque objects, with applications for non-invasive, high resolution blood flow field measurements.
2D Particle Image Velocimetry
Kim and Lee (Kim G B and Lee S J 2006, Exp. Fluids 41, 195) have measured flow in tubes with particles and blood cells as tracers using X-ray PIV. The methods taught in that study are limited to two components of the velocity (averaged over the dimension perpendicular to the image plane) within the measurement volume. The PIV algorithms used belonged to the prior art relating to optical/laser based velocimetry. These algorithms assume pulsed (instantaneous) illumination and zero out-of-plane, flow gradients and therefore fail to take into account the 3D characteristics of imaging real flows using X-rays. This results in a significant under estimation of flow velocity.
3D Particle Image Velocimetry
Recently X-ray PIV analysis has been extended to include 3D flow data. Fouras et at (Fouras A, Dusting J, Lewis R and Hourigan K et al, 2009 Journal of Applied Physics Vol. 102:064916) teach that the correlation peak represents a probability density function (PDF) of the velocity within the measurement volume. When combined with certain assumptions about the flow field, it is possible to convert this volumetric PDF of the velocity to a velocity profile. This results in the capability to measure 3D flow data from single projection X-ray images.
CT is a technique used to reconstruct an object in three-dimensional space from two dimensional projections. Typically, integrated object density in the projection direction is calculated from the X-ray attenuation, which will be proportional to the pixel intensity values on a digital projection image. The object structure is then reconstructed from projection images taken at different viewing angles, using Fourier back-projection or algebraic methods. Variants also exist for reconstruction of objects from few projection angles, which use iterative methods to reconstruct the sample's structure, often exploiting prior knowledge of the sample, for example that it is made up of a single material.
CTXV can thus deliver three component velocity measurements for complex 3D flow fields such as those found in the cardiovascular system. Single projection images are insufficient for evaluating three components of velocity. Images taken at a single projection angle contain no displacement information in the direction parallel to the X-ray beam. This limits single projection X-ray PIV to two component velocity measurements. In a method similar to CT, CTXV overcomes this limitation by using multiple projection angles. Signal-to-noise ratios can be enhanced using phase contrast imaging and phase retrieval methods.
Specifically, as in single projection X-ray PIV of the prior art, cross-correlation functions are calculated for interrogation regions within image pairs. The velocity field is reconstructed in axial slices, defined by the rows of interrogation regions for all projection angles. A three component, 2D, rectangular grid model represents the velocity field for each slice. Estimated cross correlation functions are generated for every angle and every interrogation region within each slice. The estimated cross-correlation functions are generated using convolution of the measured autocorrelation function with the velocity PDF for the interrogation region within the model. The velocity coefficients in the model are iteratively optimized, minimizing the error between measured cross-correlation function and the estimated cross-correlation functions, across all projection angles and interrogation regions simultaneously within that slice. Using this iterative approach, a model is reached which accurately represents the three component velocity field within each slice.
A relatively small number of projections are required and this is important for minimising radiation dosage. It also allows the integration of CTXV with a CT reconstruction such as described above, delivering simultaneous measurement of both form and function.
CT has the advantage of offering the best resolution and penetration of all medical imaging modalities, but also has the significant disadvantage of delivering high doses of X-rays. If not for this radiation dose concern high resolution CT would become a standard screening tool.
But even though they offer the best resolution and penetration of all medical imaging modalities, the X-ray PIV techniques of the prior art use particle images taken at a single viewing angle, which contain no particle displacement information in the direction parallel to the X-ray beam, and therefore they suffer the drawback that they are limited to two component velocity measurements. Also, no information regarding the velocity profile in the dimension perpendicular to the image plane is available, and therefore 3D measurements are not possible without prior knowledge of the flow.
There is an ongoing need to expand capabilities for measuring both form and function in terms of structure, volume and motion and provide a truer 3D reconstruction of flow fields.